Video tracking is conducted using the YOLO framework proposed in [1]}, which achieves a high detection rate in combination with real-time tracking capabilities. The implementation of YOLOFace is used, which is based on YOLOv3 [2]}. Before an input video frame \(\mathrm {X}_{\text{V}, t}\)  is processed by the video tracker, it is first resized to \(832 \times 832\)  pixels to meet the requirements of YOLOv3. We adapt the bounding box output to the \(20 \times 24\)  grid size. The grid size can be set to arbitrary values if one is willing to retrain the network. We evaluated the localization performance using several grid sizes. However, we did not find any significant differences in localization performance. We decided to set the size of the individual grid-cells to roughly match the size of a human face in the dataset.
The video tracker's output probability is denoted as \(z_{\text{V}, t}^{(i,j)} = p(\zeta _{i, j}|\mathrm {X}_{\text{V}, t})\) .
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